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Statistics > Methodology

Abstract: In many clinical trials, individuals in different subgroups have experience
differential treatment effects. This leads to individualized differences in
treatment benefit. In this article, we introduce the general concept of
predictive directions, which are risk scores motivated by potential outcomes
considerations. These techniques borrow heavily from sufficient dimension
reduction (SDR) and causal inference methodology. Under some conditions, one
can use existing methods from the SDR literature to estimate the directions
assuming an idealized complete data structure, which subsequently yields an
obvious extension to clinical trial datasets. In addition, we generalize the
direction idea to a nonlinear setting that exploits support vector machines.
The methodology is illustrated with application to a series of colorectal
cancer clinical trials.